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A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport
Transportation Research Part A: Policy and Practice ( IF 6.4 ) Pub Date : 2021-09-17 , DOI: 10.1016/j.tra.2021.07.013
Juliette Ugirumurera 1 , Joseph Severino 1 , Karen Ficenec 1 , Yanbo Ge 1 , Qichao Wang 1 , Lindy Williams 1 , Junghoon Chae 2 , Monte Lunacek 1 , Caleb Phillips 1
Affiliation  

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.



中文翻译:

用于设计和评估达拉斯-沃斯堡国际机场路边交通管理政策的建模框架

新兴的移动技术正在改变交通系统格局。这在达拉斯-沃斯堡国际机场 (DFW) 等机场尤为明显。如果不仔细分析,这些变化可能会导致机场运营效率低下且成本高昂。本文提出了一个集成旅行模式编码、需求预测和微观模拟的建模框架,使机场能够制定、模拟和评估路边交通管理政策并衡量其影响。该框架用于分析 DFW 的几种交通情景和政策:代表 2018 年观察到并预计到 2045 年的 DFW 交通模式的基线情景、交通网络公司 (TNC) 电气化政策、TNC 排队政策、增加过境乘客,仅考虑在 DFW 内使用公交车的纯公交车政策、调查采用自动驾驶汽车 (AV) 对机场运营的影响的自动驾驶汽车 (AV) 政策,以及模拟 COVID-19 情景的示例2019冠状病毒病大流行。模拟结果表明:增加 DFW 过境客流量最能推迟机场路边扩建的需要;鼓励共享出行的公交政策可以最大程度地减少路边拥堵延误;往返DFW的所有乘用车行程的自动化和电气化可最大程度地节省燃料消耗和排放;不受控制的 AV 应用会导致燃料消耗、延误和排放的最大增加,并可能需要立即扩展机场容量。

更新日期:2021-09-17
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